Senior Data Engineer

Rkotours
London
1 week ago
Create job alert
Senior Data Engineer

Our client, a well-established energy business in London, is hiring a Senior Data Engineer to support the next phase of their growth. The role is based in Mayfair and operates on a hybrid basis, with three office days and two remote days per week.


Senior Data Engineer Role Purpose

We are looking for an engineer who is responsible for building, maintaining, and evolving the data pipelines and models that underpin our supply business. This includes ingestion, transformation, validation, and exposure of data used by trading, optimisation, operations, and reporting. The role exists to provide clear ownership of the supply data stack, reduce operational and analytical friction, and allow traders, analysts, and optimisation engineers to rely on high-quality, well-understood data without constant ad-hoc intervention. This position would sit within both the supply-side of our business and the broader technology department, meaning this role also includes engaging with the technology strategy of our client as a whole.


Senior Data Engineer Key Responsibilities

  • Own the Energy Supply Data Stack.
  • Take end-to-end ownership of data pipelines supporting the supply business.
  • Ensure data is accurate, timely, and fit for both operational and analytical use in our pipelines.
  • Collaborate with supply managers to deliver insights and serve as a first point of contact.
  • Build and Maintain Robust Data Pipelines.
  • Ingest data from internal systems, market sources, and third‑party providers.
  • Implement transformations, validations, and reconciliation logic using Python and SQL.
  • Proactively identify and resolve data quality issues.
  • Collaborate Across Engineering and the Business.
  • Work closely with engineers to ensure data systems integrate with trading and optimisation platforms.
  • Support the broader engineering team by owning supply‑domain data complexity.
  • Contribute to improving standards and tooling across the data platform.
  • Develop a Deep Domain Expertise in Energy Supply and UK Markets.
  • Build a strong, working understanding of the UK energy supply industry, including market structures, products, and commercial drivers.
  • Maintain familiarity with UK electricity and gas market mechanics, settlement processes, and key regulatory frameworks.
  • Translate regulatory, commercial, and operational requirements into robust data models and pipelines.

What Were Looking For

  • Strong Python and SQL skills in data engineering contexts.
  • Experience building and maintain production data pipelines.
  • Experience working with SQL and data‑engineering environments such as Databricks or Spark.
  • Ability to work closely with non‑engineering stakeholders and translate business needs into data models.
  • Desire to become an expert in all facets of the energy systems in which they participates, from behind‑the‑meter asset optimisation to retail energy supply.

Nice to Have

  • 4+ years of related experience.
  • Experience in energy supply, trading, or market‑facing data systems.
  • Exposure to regulated or operationally critical data environments.
  • Familiarity with CHP, generation assets, or flexibility markets.

What Youll Get

  • Clear ownership of a critical part of the businesss technical foundation.
  • The opportunity to turn ad‑hoc, manual data work into robust systems.
  • Close collaboration with trading, optimisation, and operations teams.
  • A position with long‑term scope: as the company and product grow, so does your impact, responsibility, and career trajectory.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.